AI-BASED INDUSTRIAL SAFETY MONITORING AND RISK DETECTION

Abstract

Industrial environments such as manufacturing plants, construction sites, warehouses, chemical facilities, and processing units involve numerous safety hazards. Workers may be exposed to risks such as missing personal protective equipment (PPE), unsafe machinery operation, fire, smoke, hazardous zones, falls, and overcrowded work areas. Conventional safety monitoring often depends on human supervisors and periodic inspections, which may not identify dangerous situations immediately. The AI-Based Industrial Safety Monitoring and Risk Detection System is proposed as an intelligent solution that uses Artificial Intelligence and computer vision to continuously monitor industrial environments and identify potential safety risks. The proposed system analyzes real-time video streams obtained from CCTV cameras, surveillance cameras, or other authorized imaging devices. Computer vision and deeplearning models are used to detect workers, PPE items, machinery, fire, smoke, restricted areas, and other safety-related objects or conditions. The system can identify violations such as workers without helmets, safety vests, gloves, or other required protective equipment. It can also monitor predefined hazardous zones and detect when unauthorized individuals enter restricted areas. In addition to detecting individual safety violations, the system can analyze multiple visual conditions to estimate overall workplace risk. Detected events can be assigned severity levels such as low, medium, and high based on predefined safety rules. When a critical event is identified, the system can generate an alert for authorized safety personnel. The alert can contain information about the detected event, camera location, timestamp, confidence score, and captured evidence. A centralized dashboard can provide real-time monitoring of industrial areas and display detected safety violations, active alerts, risk levels, camera feeds, and historical safety statistics. Safety managers can use the dashboard to identify frequently occurring violations and determine areas that require additional training or preventive measures. Historical records can also be analyzed to identify recurring patterns and improve workplace safety planning. Overall, the proposed system aims to shift industrial safety monitoring from periodic manual inspection toward continuous AI-assisted monitoring. By combining computer vision, deep learning, rule-based risk assessment, real-time alerts, and analytics, the system can help organizations identify hazards earlier and respond more effectively. The system is intended to support, rather than replace, trained safety personnel and should be deployed with appropriate privacy, security, and workplace policies.